How to Make Great Data Visualisations

Aurélien Goutsmedt

aurelien.goutsmedt@uclouvain.be

ICHEC Brussels Management School

UC Louvain, ISPOLE

Thomas Laloux

thomas.laloux@uclouvain.be

UC Louvain, ISPOLE

May 13, 2025

What makes a great data visualisation?

One thing’s for sure: it’s never a pie chart…

What’s the plan for today?

General considerations
Not a list of rules to apply, but a series of issues to think about when producing visualisations
Practical data visualisation
Using ggplot2 in R
Putting the reflections of the first part in context

General Considerations

Healy (2018) Data Visualization: A Practical Introduction. Princeton University Press.

General discussion on common issues regarding data visualizations
Applications to social sciences
Focus on R package ggplot2 (Wickham, 2016)

Why visualizing data?

Exploring your data (identifying patterns and problems, etc…)
- Extract information intuitively, efficiently, and accurately

Revealing relationships in your data
Communicating information with precision, in a concise way
Highlighting your main findings
A more sociological dimension: bolstering credibility
… but, it is not sufficient to “look at data” for sound analysis—and it comes with some risks

First question, why visualizing data? Because you can identify many things that you can’t see by looking at a tables with many rows and columns. Important point: visualisation is not only for displaying your final results, it’s part of the research process, of exploring data and discovering patterns. Essential first step also to find problems in your data (Thomas will show you an example later): missing data, errors in some cells, etc…
Figure: same correlation between the points. Running a linear regression on your observation will give you more or less the same results. But by simply looking at the different scatterplot, you can see how different your data are. Second: non-linear pattern. Third and fourth, outliers influencing a lot your correlation.
Credibility: means you can cheat your audience/reader with nice visualization. So in a way kind of responsibility. So visualizations also comes with risks, and we can imagine there is an ethics of graphics.
Outside of cheating your audience, you can also cheat yourself. Visualizing data should be a reflexive and cautious approach to avoid displaying data in a “wrong” way.

Edward Tufte’s main principle

Graphical excellence is the well-designed presentation of interesting data—a matter of substance, of statistics, and of design. … [It] consists of complex ideas communicated with clarity, precision, and efficiency. … [It] is that which gives to the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space. … [It] is nearly always multivariate. … And graphical excellence requires telling the truth about the data. (Tufte, 1983, p. 53)

One important character in reflexions about visualizations: Edward Tufte. political scientist working notably on civil rights in the 1960s, specialized in data visualization, notably because teaching political sciences and visualizations to journalists at Yale.
One major principale of Tufte: data to ink ratio. Being to display rich data, but in a very simple graph. Avoid unnecessary details, redundancy in info, etc… A great principle, but not always to be taken absolutely. (See Tufte’s examples of great visualizations)
Beyond “data to ink ratio”:
- in a way memorable and saying something interesting
- depends of your audience ! (also some types of visualizations asked for more pedagogy than others)
- honesty about the data (don’t cheat yourself with data)
One of the difficulty: it’s a mix of rigorous presentation of data mixed with aesthetics. It’s also an issue of debating with your fellows, often helpful to see how they understand your visualizations, what you can improve, etc…

Three hurdles to great data visualisation

Bad taste: unappealing aestethics and bad design
Bad data: misuse of data, errors or missing values, etc.
Perception issues: how people perceive and process what they are looking at differs depending on cognitive and contextual factors

Bad taste

Why it’s bad?
Duplicated information
Unnecessary complexity and design features
Difficulty to extract the information correctly

Bad data (use)

Why it’s bad?
cherry-picking of data
Misleading the audience
Multiply types of visualizations, vary scales, etc., to avoid cheating yourself

Well-designed figures with little or no junk in their component parts are not by themselves a defense against cherry-picking your data or presenting information in a misleading way. Indeed, it is even possible that, in a world where people are on guard against junky infographics, the “halo effect” accompanying a wellproduced figure might make it easier to mislead some audiences. Or, perhaps more common, good aesthetics does not make it much harder for you to mislead yourself as you look at your data.
scholars who knew the World Values Survey data underlying the graph noticed something else. The graph reads as though people were asked to say whether they thought it was essential to live in a democracy, and the results plotted show the percentage of respondents who said “Yes,” presumably in contrast to those who said “No.” But in fact the survey question asked respondents to rate the importance of living in a democracy on a ten-point scale, with 1 being “Not at all Important” and 10 being “Absolutely Important.”

Perception issues: some examples

Perception issues: colors

Hue: What color is it?
Red, blue, green, yellow, etc.
Chroma (or Saturation): How pure or intense is the color?
washed-out or grayish vs. vivid or rich
Luminance (or Lightness/Brightness): How light or dark is the color?
close to white vs. close to black

Colors are tricky. Our perception of colors may be different (you have different types of color-blindness or color vision deficiency). But more generally, our perception of colors may be altered by the vincinity of colors, shadow, etc… Just an example here.
Five palettes generated from R’s color space library. From top to bottom, the sequential grayscale palette varies only in luminance, or brightness. The sequential blue palette varies in both luminance and chrominance (or intensity). The third sequential palette varies in luminance, chrominance, and hue. The fourth palette is diverging, with a neutral midpoint. The fifth features balanced hues, suitable for unordered categories.
The upshot is that we should generally not pick colors in an ad hoc way. It is too easy to go astray. In addition to the considerations we have been discussing, we also want to avoid producing plots that confuse people who are color-blind, for example. Fortunately, almost all the work has been done for us already. Different color spaces have been defined and standardized in ways that account for these uneven or nonlinear aspects of human color perception
Choose appropriate palettes for what you want (types of palette like gradient, diverging, etc… means something). Taking care of different color-blindness.

Perception issues: shapes and scales

Perception issues: shapes and scales

Practical Data Visualisation with ggplot2

What is ggplot2 ?

ggplot2 is a data visualization package for R developed by Hadley Wickham (2016).
It lets you build complex plots from simple building blocks.
Inspired by the Grammar of Graphics (Wilkinson, 2011) — a system for describing and thinking about graphics.
- A consistent syntax for building complex graphics by combining simple components
- Shifts the focus from choosing predefined chart types to systematically defining what to show and how to show it.

Why should you use ggplot2?

Powerful and flexible
Grammar of Graphics:
Stems from a coherent logics of visualization, which helps to develop reflexivity
Customize almost every visual aspect
Intuitive syntax once you understand the grammar
Active community, extensive documentation, updates, and extension (packages)
Integrated with R and the tidyverse suite

How ggplot2 Works

Plots as a combination of different layers
ggplot2 builds plots by layers, each layer adds a different component to the plot
The concept behind ggplot2 divides plot into three different fundamental parts: Plot = Data + Variable + Geometric Object.
Then you customize plot details (the theme) with elements e.g., the scales, grid, labels, and legends using specific functions
\(\Rightarrow\) Plots are built incrementally by adding components piece by piece

Coherent logics: push you to ask yourself the good questions : which geom will I use ? With wich data? It is useful to think about the purpose of each layer before it is added. In general, there are three purposes for a layer:

To display the data. We plot the raw data for many reasons, relying on our skills at pattern detection to spot gross structure, local structure, and outliers. This layer appears on virtually every graphic. In the earliest stages of data exploration, it is often the only layer.

To display a statistical summary of the data. As we develop and explore models of the data, it is useful to display model predictions in the context of the data. Showing the data helps us improve the model, and showing the model helps reveal subtleties of the data that we might otherwise miss. Summaries are usually drawn on top of the data.

To add additional metadata: context, annotations, and references. A metadata layer displays background context, annotations that help to give meaning to the raw data, or fixed references that aid comparisons across panels. Metadata can be useful in the background and foreground.

How ggplot2 Works: Three main Components

Data: The dataset used to generate the plot

Mapping (aesthetics): Specifies which variables from the dataset are mapped to visual properties like position, color, or size
Geom: Refers to the geometric objects (e.g., points, lines, bars) that represent data on the plot \(\Rightarrow\) This defines the type of plot you want to create.

You can combine multiple geoms — for example, points with a regression line

Then the details can be refined!

Presenting the dataset : ISPOLE publications in DIAL

Rows: 2,779
Columns: 50
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Example Plot: Number of Publications by Year

Start with the data and the mapping:

ggplot(data = data_ispole, aes(x = annee))

Example Plot: Number of Publications by Year

Identify the Issue : Bad Encoding?

annee
Delreux, Tom;";";";";";One big conversation: the EU’s climate diplomacy across the international regime complex on climate change: the case of the Paris Agreement Negotiations.;Anglais;";";";";http://hdl.handle.net/2078.1/260592;";";";";";";";";";";";";The European Union in International Affairs, Online, du 26/05/2021 au 28/05/2021;";";";";";European Union ¦ climate diplomacy ¦ international climate politics;";";";";CEE;";";";" boreal:270632;Communication à un colloque (Conference Paper);Présentation orale avec comité de sélection;";2021;";";UCL - SSH/SPLE - Institut de sciences politiques Louvain-Europe;Squevin, Pierre Louis Daniel
Cambré, Bart
Rihoux, Benoît;";";";";";The impact of the Stemtest/Test électoral on voting in the Belgian general elections of 2019;Anglais;";";";";http://hdl.handle.net/2078.1/224165;";";";";";";";";";";";";the Eighth edition of the conference Belgium: The state of the federation, Bruxelles, 19/12/2019;";";";";";";";";";";CESPOL;";";";" boreal:224208;Communication à un colloque (Conference Paper);Présentation orale avec comité de sélection;";2019;";";UCL - SSH/SPLE - Institut de sciences politiques Louvain-Europe;Moyson, Stéphane;";";";";";The individual psychology of policy learning in the advocacy coalition framework: Information use, egocentrism and self-esteem in the European liberalization of Belgian network industries;Anglais;";";";";http://hdl.handle.net/2078.1/224208;";";";";";";";";";";";";General Conference of the European Consortium for Political Research (ECPR), Wroclaw, Poland, du 04/09/2019 au 07/09/2019;";";";";";CMAP/POL;";";";";SPLE;F.R.S.-FNRS;ECODEMO;PDR;ECODEMO boreal:222588;Communication à un colloque (Conference Paper);Présentation orale avec comité de sélection;";2019;";";UCL - SSH/SPLE - Institut de sciences politiques Louvain-Europe;Ait-Chaalal, Amine;";";";";";The interactions between globalization, inequality and (in)security in the European, African and Middle Eastern contexts: traditional views and new problems. ;Anglais;";";";";http://hdl.handle.net/2078.1/222588;";";";";";";";";";";";";New challenges for the 21st Century, Universidade de Brasilia , 02/10/2019;";";";";";Europe Middle East Africa International Relations;";";";";ISPOLE ¦ CECRI;";";";" boreal:243889;Communication à un colloque (Conference Paper);Présentation orale avec comité de sélection;";2019;";";UCL - SSH/SPLE - Institut de sciences politiques Louvain-Europe;André, Lyla;";";";";";The role of the EU as a leading donor in framing the humanitarian response to the Syrian “Refugee crisis” in Lebanon;Anglais;";";";";http://hdl.handle.net/2078.1/243889;";";";";";";";";";";";";EISA Annual Conference A Century of Show and Tell: The Seen and the Unseen of IR, Sofia, Bulgaria, du 10/09/2019 au 14/09/2019;";";";";";European Union - Humanitarian aid - Refugee protection;";";";";European Management Syrian refugee crisis;Université Catholique de Louvain;FSR;ISPOLE / Germac;"The Eu management of the Syrian refugee crisis" boreal:215145;Communication à un colloque (Conference Paper);Présentation orale avec comité de sélection;";2019;";Accès libre;UCL - SSH/SPLE - Institut de sciences politiques Louvain-Europe;Reuchamps,
Van Ingelgom, Virginie
mémoire
Burns, Charlotte;";";";";";The involvement of the European Parliament in UN climate negotiations;Anglais;";";";";http://hdl.handle.net/2078.1/200022;";";";";";";";";";";";";European Union in International Affairs VI, Brussels, du 16/05/2018 au 18/05/2018;";";";";";Climate change ¦ Climate Diplomacy ¦ Conference of the Parties (COP) ¦ European Parliament ¦ European Union ¦ parliamentarization ¦ United Nations Framework Convention on Climate Change (UNFCCC) ¦ climate change ¦ climate diplomacy ¦ Conference of the Parties ¦ European Parliament ¦ European Union ¦ parliamentarization ¦ Unitied Nations Framework Convention on Climate Change;";";";";CEE;";";";" boreal:200030;Communication à un colloque (Conference Paper);Présentation orale avec comité de sélection;";2018;";Accès restreint;UCL - SSH/SPLE - Institut de sciences politiques Louvain-Europe;Delreux, Tom
Dodeigne, Jérémy
Heyvaert, Pauline
Heyvaert, Pauline
Pellon, Gaëlle
Haarscher, Guy
Meier, Petra;";";";";";Is it male chauvinism, party affiliation or political apathy? Gender-based preferential voting in Belgium;Anglais;";";";";http://hdl.handle.net/2078.1/142239;";";";";";";";";";";";";ECPR Joint Sessions of Workshops, Mainz, du 11/03/2013 au 16/03/2013;";";";";";";";";";";";";";";" boreal:138427;Communication à un colloque (Conference Paper);Présentation orale avec comité de sélection;";2013;";";UCL - SSH/SPLE - Institut de sciences politiques Louvain-Europe;Rosoux, Valérie;";";";";";Is reconciliation negotiable ?;Anglais;";";";";http://hdl.handle.net/2078.1/138427;";";";";";";";";";";";";Conférence organisée par le Durham Global Security Institute (DGSI), Durham;";";";";";";";";";";";";";";" boreal:142882;Communication à un colloque (Conference Paper);Présentation orale avec comité de sélection;";2013;";Accès libre;UCL - SSH/SPLE - Institut de sciences politiques Louvain-Europe;Laborderie, Vincent;";";";";";Judging the hypothesis of a ‘Break-up’ of Federal Belgium : comparative perspectives;Anglais;";";";";http://hdl.handle.net/2078.1/142882;";";";";";";";";";";";";State of the Federation, Louvain-la-Neuve, 18/10/2013;";";";";";Belgique ¦ Flandre ¦ Séparatisme ¦ Indépendance ¦ Reconnaissance internationale;";";";";ISPOLE;";";";" boreal:136089;Communication à un colloque (Conference Paper);Présentation orale avec comité de sélection;";2013;";";UCL - SSH/SPLE - Institut de sciences politiques Louvain-Europe;Gijs, Anne-Sophie;";";";";";La Belgique et l'anticommunisme au Congo (1945-1961): une histoire aux répercussions bilatérales et internationales;Français;";";";";http://hdl.handle.net/2078.1/136089;";";";";";";";";";";";";Workshop: Current Perspectives in Congo Research, Université Saint-Louis, Bruxelles, 20/12/2013;";";";";";";";";";";";";";";" boreal:134050;Communication à un colloque (Conference Paper);";";2013;";Accès interdit;UCL - SSH/LouRIM - Louvain Research Institute in Management and Organizations ¦ UCL - SSH/SPLE - Institut de sciences politiques Louvain-Europe;Raone, Julien

Example Plot: Number of Publications by Year

(re)Start with the data and the mapping:

data_ispole %>%
  mutate(annee = as.numeric(annee)) %>%
  ggplot(aes(x = annee))

Example Plot: Number of Publications by Year

Now add the geom

data_ispole %>%
  mutate(annee = as.numeric(x = annee)) %>%
  ggplot(data = ., aes(x = annee)) +
  geom_bar()

Example Plot: Number of Publications by Year

Choose another geom

data_ispole %>%
  mutate(annee = as.numeric(annee)) %>%
  ggplot(aes(x = annee)) +
  geom_boxplot()

Example Plot: Number of Publications by Year

Now add another mapping

data_ispole %>%
  mutate(annee = as.numeric(annee)) %>%
  ggplot(data = ., aes(x = annee, fill = peer_review)) +
  geom_bar()

Example Plot: Number of Publications by Year

Now add the details

First, install some useful extensions to ggplot2

library(ggpubr) # additional themes
library(hrbrthemes) # additional themes
library(ggsci) # additional color palettes
library(see) # additional color palettes
library(gghighlight) # highlighting features

data_ispole <- data_ispole %>% 
  mutate(annee = as.numeric(annee))

Example Plot: Number of Publications by Year

Now add the details

data_ispole %>%
  ggplot(data = ., aes(x = annee)) +
  geom_bar(fill = "darkseagreen", color = "black") +
  scale_x_continuous(breaks = c(2010, 2024)) +
  theme(panel.grid.major.y = element_line(color = "black", linetype = "dashed"),
        panel.grid.minor.y = element_line(color = "red", linetype = "dotted"))

Example Plot: Number of Publications by Year

Now add the details

ggplot(data = data_ispole, aes(x = annee)) +
  geom_bar(fill = "darkseagreen", color = "black") +
  theme_pubclean() +
  theme(plot.title = element_text(size = 20, hjust = 0.5)) +
  labs(x = NULL, y = NULL, title = "Evolution of ISPOLE's Publication Activity Over Time")

Title Length Across Publication Types

What would be a good way to visualize this?

type_de_publication
n_titre

Article de périodique (Journal article) :999
Min. : 0.00

Communication à un colloque (Conference Paper) :693
1st Qu.: 53.00

Contribution à ouvrage collectif (Book Chapter):650
Median : 78.00

Monographie (Book) :162
Mean : 86.05

Document de travail (Working Paper) :151
3rd Qu.: 108.00

Thèse (Dissertation) : 79
Max. :3431.00

(Other) : 45
NA

	type_de_publication	n_titre
	Article de périodique (Journal article) :999	Min. : 0.00
	Communication à un colloque (Conference Paper) :693	1st Qu.: 53.00
	Contribution à ouvrage collectif (Book Chapter):650	Median : 78.00
	Monographie (Book) :162	Mean : 86.05
	Document de travail (Working Paper) :151	3rd Qu.: 108.00
	Thèse (Dissertation) : 79	Max. :3431.00
	(Other) : 45	NA

Title Length Across Publication Types

ggplot(data = data_ispole, aes(x = type_de_publication, y = n_titre))

Visualizing Title Length Across Publication Types

Visualize the data through a boxplot

ggplot(data = data_ispole, aes(x = type_de_publication, y = n_titre)) +
  geom_boxplot() +
  theme_pubclean()

How could we improve this plot?

Visualizing Title Length Across Publication Types

New Visualisation
Code

plot <- data_ispole2 %>%
  mutate(
    type_de_publication_fr = str_replace(type_de_publication, " \\s*\\([^\\)]+\\)", ""),
    type_de_publication_en = gsub("(?<=\\()[^()]*(?=\\))(*SKIP)(*F)|.", "", type_de_publication, perl = T)
  ) %>%
  ggplot(aes(x = reorder(type_de_publication_en, n_titre), y = n_titre, fill = reorder(type_de_publication_en, n_titre))) +
  geom_boxplot(show.legend = F) +
  theme_pubclean(base_family = "Century Gothic") +
  coord_flip() +
  scale_y_continuous(name = "Number of Words in Titles", expand = c(0, 0)) +
  theme(
    axis.title.y = element_blank(),
    panel.grid.major.y = element_blank(),
    panel.grid.major.x = element_line(color = "grey", linetype = "dotted"),
    plot.title.position = "plot", # Align relative to full plot area
    plot.title = element_text(hjust = 0)
  ) +
  scale_fill_oi() +
  ggtitle("Number of words in the title according to the type of Publication")
plot

Visualizing Title Length Across Publication Types

Spot the modifications!

Visualizing Title Length Across Publication Types

Add faceting by another variable (and another geom)

Visualizing Title Length Across Publication Types

Add faceting by another variable (and another geom)

plot +
  geom_jitter(show.legend = F, alpha = 0.5, shape = 21, size = 0.2) +
  facet_wrap(. ~ peer_review) +
  theme_ipsum(base_family = "Century Gothic") +
  theme(
    axis.title.y = element_blank(),
    plot.title.position = "plot", # Align relative to full plot area
    plot.title = element_text(hjust = 0)
  )

General Rule

(Almost) always use faceting when showing differences across a variable

General Rule

Exercice

On Paper (No Computers)

How would you visualize the relationship between the number of conference papers and the number of published outputs (books, journal articles, and book chapters)?
How would you compare peer-reviewed and non–peer-reviewed publications in this context?
In both cases, how would you structure your ggplot2 code accordingly?

Exercice

Exercice

References

Healy, K. (2018). Data visualization: A practical introduction. Princeton University Press.

Tufte, E. R. (1983). The Visual Display of Quantitative Information (1st edition). Graphics Press USA.

Wickham, H. (2016). ggplot2: Elegant graphics for data analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org

Wilkinson, L. (2011). The grammar of graphics. In Handbook of computational statistics: Concepts and methods (pp. 375–414). Springer.